Banks and other financial institutions typically attempt to identify and quantify risks associated with their business dealings. Two types of risks typically identified and quantified are credit risk and market risk. As their names imply, credit risk relates to risk associated with giving or receiving credit and market risk relates to risk associated with changes in market conditions.
A third type of risk, which banks and other financial institutions are just now beginning to address, is operational risk. One definition of operational risk promulgated by the Basel Committee on Banking Supervision (hereinafter “Basel Committee”) is that operational risk is a risk component bother than credit or market risk and which is “the risk of direct or indirect loss resulting from inadequate or failed internal processes, people and systems or from external events”. The aforementioned definition will be adopted for the purposes of this application.
In any case, the Basel Committee proposes a number of approaches for allocating operating risk capital. In following the typical banking methodology of identifying and quantifying risk, these approaches include, but are not limited to, the Basic Indicator, the Standardized approach, the Internal Measurement approach, and the Loss Distribution approach. However, none of these approaches appears to provide for the aggregation of individual risk factors of a plurality transactions on a transaction by transaction basis in order to identify the relative risk of each transaction. In other words, while the various approaches proposed by the Basel Committee attempt to identify and quantify operational risk, such approaches do not appear to provide a mechanism for easily ranking the relative risk of a number of transactions without trying to explicitly quantify such risk (i.e., in terms of capital loss).
Other risk analysis methodologies found in the financial area include, for example, the following:
U.S. Pat. No. 6,119,103, issued Sep. 12, 2000, to Basch et al. relates to financial risk prediction systems and methods.
U.S. Pat. No. 5,978,778, issued Nov. 2, 1999, to O'Shaughnessy relates to automated strategies for investment management.
U.S. Pat. No. 6,003,018, issued Dec. 14, 1999, to Michaud et al. relates to a method for evaluating an existing or putative portfolio having a plurality of assets.
U.S. Pat. No. 5,812,987, issued Sep. 27, 1998, to Luskin et al. relates to an invention for managing assets in one or more investment funds over a specified time.
U.S. Pat. No. 6,055,517, issued Apr. 25, 2000, to Friend et al. relates to a method of simulating future cash flow for a given asset allocation under a variety of economic conditions and measuring the frequency of failure of the cash flow to avoid one or more predefined risks.
U.S. Pat. No. 5,729,700, issued Mar. 17, 1998, to Melnikoff relates to a portfolio selector for selecting an investment portfolio from a library of assets based on investment risk and risk-adjusted return.
U.S. Pat. No. 5,884,287, issued Mar. 16, 1999, to Edesess relates to a computer-implemented system and method to create an optimal investment plan (given wealth goals stated in probabilistic form) and to display the resulting probability distributions of wealth accumulations at future times.
Further, various methods of risk or failure analysis have been proposed for use in such fields as manufacturing, aviation, and disk drive monitoring. These methodologies include, for example, the following:
U.S. Pat. No. 5,828,583, issued Oct. 27, 1998, to Bush et al. relates to a method for predicting an imminent failure of a disk drive.
U.S. Pat. No. 5,956,251, issued Sep. 21, 1999, to Atkinson et al. relates to a process of establishing valid statistical dimensional tolerance limits for designs of detail parts that will enable accurate prediction of an economically acceptable degree of non-conformance of a large flexible end item assembly.
Further still, in one type of inventory tracking methodology there is maintained an A-B-C classification of items kept in store. Class A items have to be monitored very closely and should have some safety stock (because it is very costly to be out of stock of this class of item). Class B items are monitored less closely (because it is not as costly to be out of stock of this class of item), and so on. Moreover, in one type of scheduling methodology there is maintained a prioritization of jobs that have to be done by the same resource or machine. Jobs are ranked, or prioritized, based on the dimensions of the workpiece and/or the time it takes to do the job.